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The vector representation of the entities and relations can be used for different machine learning applications. In representation learning, knowledge graph embedding (KGE), also referred to as knowledge representation learning (KRL), or multi-relation learning, [1] is a machine learning task of learning a low-dimensional representation of a ...
In order to allow the use of knowledge graphs in various machine learning tasks, several methods for deriving latent feature representations of entities and relations have been devised. These knowledge graph embeddings allow them to be connected to machine learning methods that require feature vectors like word embeddings. This can complement ...
Attention in Machine Learning is a technique that mimics cognitive attention. In the context of learning on graphs, the attention coefficient measures how important is node to node . Normalized attention coefficients are computed as follows:
The goal of many graph representation learning techniques is to produce an embedded representation of each node based on the overall network topology. [ 39 ] node2vec extends the word2vec training technique to nodes in a graph by using co-occurrence in random walks through the graph as the measure of association. [ 40 ]
A chain graph is a graph which may have both directed and undirected edges, but without any directed cycles (i.e. if we start at any vertex and move along the graph respecting the directions of any arrows, we cannot return to the vertex we started from if we have passed an arrow). Both directed acyclic graphs and undirected graphs are special ...
Meta-representation means the knowledge representation language is itself expressed in that language. For example, in most Frame based environments all frames would be instances of a frame class. That class object can be inspected at runtime, so that the object can understand and even change its internal structure or the structure of other ...
Undirected hypergraphs are useful in modelling such things as satisfiability problems, [5] databases, [6] machine learning, [7] and Steiner tree problems. [8] They have been extensively used in machine learning tasks as the data model and classifier regularization (mathematics). [9]
In machine learning, the term tensor informally refers to two different concepts (i) a way of organizing data and (ii) a multilinear (tensor) transformation. Data may be organized in a multidimensional array (M-way array), informally referred to as a "data tensor"; however, in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector ...